Automated major depressive disorder detection using melamine pattern with EEG signals

Aydemir, Emrah and Tuncer, Tucker and Dogan, Sengul and Gururajan, Raj ORCID: https://orcid.org/0000-0002-5919-0174 and Acharya, U. Rajendra (2021) Automated major depressive disorder detection using melamine pattern with EEG signals. Applied Intelligence, 51. pp. 6449-6466. ISSN 0924-669X


Abstract

Major depressive disorder (MDD) is one of the most common modern ailments affected huge population throughout the world.The electroencephalogram (EEG) signal is widely used to screen the MDD. The manual diagnosis of MDD using EEG is timeconsuming, subjective and may cause human errors. Therefore, nowadays various automated systems have been developed todiagnose MDD accurately and rapidly. In this work, we have proposed a novel automated MDD detection system using EEGsignals. Our proposed model hasthreesteps: (i) Melamine pattern and discrete wavelet transform (DWT)- based multileveledfeature generation, (ii) selection of most relevant features using neighborhood component analysis (NCA) and (iii) classificationusing support vector machine (SVM) and k nearest neighbor (kNN) classifiers. The novelty of this work is the application ofmelamine pattern. The molecular structure of melamine (also named chemistry spider- ChemSpider) is used to generate 1536features. Also, various statistical features are extracted from DWT coefficients. The NCA is used to select the most relevantfeatures and these selected features are classified using SVM and kNN classifiers. The presented model attained greater than 95%accuracies using all channels with quadratic SVM classifier. Our results obtained highest classification accuracy of 99.11% and99.05% using Weighted kNN and Quadratic SVM respectively using A2A1 EEG channel. We have developed the automateddepression model using a big dataset and yielded high classification accuracies. These results indicate that our presented modelcan be used in mental health clinics to confirm the manual diagnosis of psychiatrists.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published online: 28 April 2021. Permanent restricted access to ArticleFirst version, in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Date Deposited: 30 Apr 2021 00:43
Last Modified: 27 Sep 2021 04:43
Uncontrolled Keywords: melamine pattern; statistical feature generation; major depression detection; NCA selector; EEG signal processing
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460206 Knowledge representation and reasoning
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Socio-Economic Objectives (2020): 20 HEALTH > 2099 Other health > 209999 Other health not elsewhere classified
Identification Number or DOI: https://doi.org/10.1007/s10489-021-02426-y
URI: http://eprints.usq.edu.au/id/eprint/41923

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